Abstract
Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological ‘fingerprints’. Such methods have the potential to detect clinically relevant subtypes, track an individual’s disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.
Highlights
Heterogeneity is an underlying characteristic of dementia, in presentation and progression
This paucity of treatments, in combination with the rapid ageing of the global population, adds to the societal burden of dementia.[2]. This motivates re-evaluation of common experimental approaches in dementia research and clinical trials with the goal of optimising statistical design. In this Update, we review current and emerging neuroimaging analysis methods which can account for the intrinsic heterogeneity to help further our understanding of dementia and improve the prospect of developing effective treatments
Heterogeneity in dementia Dementia is characterised by progressive cognitive decline, over and above that seen in normal ageing, with subsequent impact on activities of daily living
Summary
Heterogeneity is an underlying characteristic of dementia, in presentation and progression. Data-driven methods have enabled the estimation of disease subtypes from neuroimaging data, a promising way to disentangle heterogeneity by grouping patients by distinctive neurobiological and cognitive characteristics,[46] and disease progression.[47,48] For instance, hierarchical clustering algorithms have been utilised to understand variation in cortical thickness,[49,50] grey matter[51] and progressive neurodegeneration.[52] Clustering techniques employed on large AD neuroimaging datasets have suggested that there are disease subtypes with distinct patterns of cortical thinning.
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